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CVE Record

CVE-2021-29530: Invalid validation in `SparseMatrixSparseCholesky`

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

LowCVSS 2.5Not KEV-listedUpdated
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Glexia's TakeAutomated analysislow

Security readout for executives and security teams

Plain-English summary

CVE-2021-29530 is a low-severity TensorFlow crash issue. A user who can run TensorFlow operations may provide an invalid permutation to a sparse Cholesky operation and trigger a null pointer dereference, causing limited availability impact. The sources do not show data theft, privilege escalation, or active exploitation.

Executive priority

Handle through normal patch management unless TensorFlow is exposed in shared or user-controlled execution environments. Business risk is mainly localized service disruption, not compromise, based on the provided evidence.

Technical view

The flaw is improper validation in `tf.raw_ops.SparseMatrixSparseCholesky`. `ValidateInputs` uses `OP_REQUIRES`, which returns only from the validation function; the caller then continues execution after a failed check. This can lead to a null pointer dereference. Fixed releases were planned for TensorFlow 2.5.0 and supported 2.4.2, 2.3.3, 2.2.3, and 2.1.4 branches.

Likely exposure

Exposure is most likely in environments running vulnerable TensorFlow versions where users, jobs, notebooks, or services can execute TensorFlow ops with attacker-controlled tensors. The provided sources do not establish normal remote internet exposure.

Exploitation context

CVSS indicates local access, low privileges, high attack complexity, no user interaction, and low availability impact only. The source bundle marks KEV as false, and no cited source reports active exploitation.

Researcher notes

The key nuance is control-flow misuse of `OP_REQUIRES`: validation failure exits `ValidateInputs`, not the caller. Treat impact as null pointer dereference availability loss. Do not infer broader memory corruption or remote exploitability from the supplied sources.

Mitigation direction

  • Upgrade TensorFlow to 2.5.0 or the patched supported branch release.
  • Prioritize patched 2.4.2, 2.3.3, 2.2.3, or 2.1.4 where applicable.
  • Inventory containers, notebooks, and dependency lockfiles for vulnerable TensorFlow versions.
  • Restrict untrusted users from running arbitrary TensorFlow operations on shared infrastructure.
  • Monitor TensorFlow advisory updates for any revised affected-version guidance.

Validation and detection

  • Confirm deployed TensorFlow versions in runtime environments and build artifacts.
  • Check whether workloads expose `SparseMatrixSparseCholesky` to untrusted tensor inputs.
  • Verify patched versions are present after dependency rebuilds and redeployments.
  • Run existing ML regression tests after upgrading TensorFlow.
  • Review shared notebook or tenant environments for arbitrary TensorFlow op execution.
Prepared
Confidence
high
Sources
4

Public sources used

Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.

Potential ATT&CK relevance

Conservative CVE-to-ATT&CK context

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ATT&CK lookup starting points

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cwe · low confidence lookup

CWE-476: Exact CWE lookup

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cve · low confidence lookup

CVE-2021-29530 mapping review

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Vulnerability profileCVE Program record
Severity
Low
CVSS
2.5 (3.1)
Known Exploited
No
Published

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Official CVE source material

CNA and ADP enrichment extracted from CVE v5

These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.

1CVSS vectors
0Timeline events
0ADP providers
3Source links

CVSS vector scores

1 official score

We collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.

ScoreVersionSeverityVectorExploitImpactSource
2.5CVSS 3.1LowCVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L11.4Primary CVE score

Vulnerability scoring details

Base CVSS 3.1 score

2.5Low
CVSS 3.1 vector shape for CVE-2021-29530Attack VectorAttack ComplexityPrivileges RequiredUser InteractionScopeConfidentiality ImpactIntegrity ImpactAvailability Impact

Vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Attack Vector
NetworkAdjacentLocalPhysical
Attack Complexity
LowHigh
Privileges Required
NoneLowHigh
User Interaction
NoneRequired
Scope
ChangedUnchanged
Confidentiality Impact
HighLowNone
Integrity Impact
HighLowNone
Availability Impact
HighLowNone

Source materials

Affected products

Products and packages named in the record

VendorProductVersion / packageStatus
tensorflowtensorflow< 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2Listed
Weakness

CWE details

CWE-476

CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.

CWE-476 · source CWE mapping

NULL Pointer Dereference

NULL Pointer Dereference represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.

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